Influence of Soil Strength on Optimal Embedment Depth of Geosynthetic Reinforcement Layer in Granular-Reinforced Foundations
Bibliographic record
Abstract
The effect of soil friction angle value on the optimal embedment depth of a single geosynthetic reinforcement layer in granular foundations was investigated with a numeric simulation approach. The numeric model was validated against the measured results of reduced-scale plane strain model foundations tested with two footing width sizes. The model foundations were constructed with gravel-sized round aggregates and were subjected to the concentrated vertical load of shallow footings analogous to a track–ballast system. After the validation of the numeric model, a series of parametric analyses was carried out with an idealized, full-scale foundation model to investigate the dependency of the optimal reinforcement embedment depth on the soil friction angle value. The optimal reinforcement depth in field-scale reinforced foundations under strip footings is between 10% and 15% of footing width, depending on the site soil friction angle value. The optimal reinforcement depth is found to be slightly closer to the footing in a weaker soil. The range of optimum reinforcement depth values obtained from this study is shallower than those suggested in some earlier studies using reduced-scale model foundations. Optimum reinforcement depth values obtained merely on the basis of the results of reduced-scale tests may be overestimated because of the unrealistically low overburden pressure compared with the field-scale foundations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".